A Living Review of Machine Learning for Particle Physics (2021-02-02T00:00:00.000000Z)

TL;DR

This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.

Abstract

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.

Authors

M. Feickert

1 papers

B. Nachman

6 papers

TL;DR

Abstract

Authors

References375 items

Generative Adversarial Networks

Development of convolutional neural networks for an electron-tracking Compton camera

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

The Tracking Machine Learning Challenge: Throughput Phase

Unveiling the pole structure of S-matrix using deep learning

CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors

Invariant polynomials and machine learning

Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning

End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data

Autoencoders for unsupervised anomaly detection in high energy physics

Higgs analysis with quantum classifiers

Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities

Understanding Event-Generation Networks via Uncertainties

Compressing PDF sets using generative adversarial networks

Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics

Comparing weak- and unsupervised methods for resonant anomaly detection

Segmentation of EM showers for neutrino experiments with deep graph neural networks

Shower Identification in Calorimeter using Deep Learning

Charged Particle Tracking via Edge-Classifying Interaction Networks

Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks

Generalization capabilities of translationally equivariant neural networks

Elvet - a neural network-based differential equation and variational problem solver

Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Sparse deconvolution methods for online energy estimation in calorimeters operating in high luminosity conditions

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions

Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC

Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

Bayesian Nonparametric Inference of the Neutron Star Equation of State via a Neural Network

Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices

Unsupervised event classification with graphs on classical and photonic quantum computers

Deep Learning Strategies for ProtoDUNE Raw Data Denoising

Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC energies using machine learning

Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference

A neural network classifier for electron identification on the DAMPE experiment

Sequence-based Machine Learning Models in Jet Physics

Point cloud transformers applied to collider physics

Learning to isolate muons

Pulling the Higgs and top needles from the jet stack with feature extended supervised tagging

Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors

Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of machine learning

Development of a Vertex Finding Algorithm using Recurrent Neural Network

A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

A convolutional-neural-network estimator of CMB constraints on dark matter energy injection

Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using machine learning techniques

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Learning from many collider events at once

Hashing and metric learning for charged particle tracking

A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory

Lattice gauge equivariant convolutional neural networks

How to GAN Higher Jet Resolution

Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits

Unsupervised in-distribution anomaly detection of new physics through conditional density estimation

Image-Based Jet Analysis

Jet tagging in the Lund plane with graph networks

Uncovering hidden patterns in collider events with Bayesian probabilistic models

Particle Track Reconstruction using Geometric Deep Learning

Deep learning jet modifications in heavy-ion collisions

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

How to GAN Event Unweighting

Explainable machine learning of the underlying physics of high-energy particle collisions

Deep-Learning-Based Kinematic Reconstruction for DUNE

Deep learning analysis of deeply virtual exclusive photoproduction

Simulating the time projection chamber responses at the MPD detector using generative adversarial networks

Distributed Training and Optimization Of Neural Networks

Equivariant energy flow networks for jet tagging